Abstract: New fault modes will continuously emerge in the long-term operation and service process of machinery equipment, which poses higher requirement of the continual learning and lifelong diagnosis capability for intelligent diagnostic models. Lifelong intelligent diagnosis technology driven by class-incremental learning provides new approaches to ensure the full lifecycle safe operation of high-end equipment. However, existing class-incremental learning methods cannot address the problem of efficient incremental transfer diagnosis under the circumstance of cross-operating conditions. To this end, this paper proposes a cross-domain lifelong intelligent diagnostic method driven by meta-class-incremental transfer learning. An enhanced feature extractor is developed via integrating deep residual networks with a convolutional block attention feature fusion module to achieve deep feature extraction and fusion across channel and spatial dimensions. A multi-level knowledge distillation strategy is constructed through combining feature-level and decision-level knowledge distillation mechanisms to effectively address catastrophic forgetting issues in incremental transfer diagnostic scenarios. A meta-class-incremental parameter learning mechanism is proposed by innovatively incorporating the idea of meta-learning into class-incremental learning framework, thus improving the model generalization ability for incremental transfer diagnosis. Experiment validations were conducted on subway train transmission system test rig. Results show that the proposed method achieves an average diagnostic accuracy of 94.96% and an average forgetting rate of 3.85% across different incremental transfer diagnostic scenarios, and outperforms state-of-the-art class-incremental learning methods, offering insights for achieving lifelong intelligent fault diagnosis in full lifecycle health management of high-end equipment.